Comparing Objective And Subjective Measures Of Exercise Stress In Female Collegiate Ice Hockey Players
Bibliographic record
Abstract
Quantifying exercise stress experienced by ice hockey players during training and competition is essential for practitioners in supporting coaches. Exercise stress can be quantified using both subjective and objective methods to derive a training load. However, there remains a paucity of inquiry into the association between indices of subjective and objective training loads in collegiate hockey players. PURPOSE: To examine the association between a subjective and objective measure of exercise stress in female collegiate ice hockey players. METHODS: A total of 21 healthy university female ice hockey players with an age (mean ± SD) of 20.4 ± 1.7 y, a height of 166.3 ± 4.7 cm, and a body weight of 66.4 ± 7.3 kg, volunteered to be participants over a two-week period. Each participant wore a chest-strap heart rate monitor during on-ice training and competition. An objective measure of exercise stress was quantified using heart rate dynamics and was calculated using Edwards training load. A subjective measure of exercise stress was quantified using a sessional rating of perceived exertion (sRPE) and was multiplied by time (min) to derive a training load. Indices of exercise stress were recorded from the beginning of the dry-land warm-up and finished upon completion of the off-ice cool-down. The association between sRPE and HR derived training loads were examined using Linear Regression. Significance was declared as a probability of p < 0.05. The study was approved by the research ethics review board of the University of Windsor. RESULTS: Weekly HR-derived training load was (mean ± SD) 300.3 ± 141.5 (AU). Weekly sRPE training load was (mean ± SD) 830.9 ± 466.4 (AU). A significant association between sRPE and HR-derived training loads were observed as displayed though a coefficient of determination of (R2 = 0.70, p < 0.0001) and a Pearson correlation coefficient of (r 95% CI = 0.84, 0.80: 0.87). CONCLUSION: The results from study demonstrate sRPE and HR derived training loads, from on-ice training and competition, are significantly associated during regular season play. Practitioners may wish to choose a singular method that is both feasible and time sensitive to better support coaches and the integrated support staff.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".